Insights Into A Movie Review

Our Sentiment Analysis Model has achieved an accuracy of 89.3% in classifying reviews from the Internet Movie Database (IMDB). That’s good, amazing – the reviews are often tricky to read. But 1 review of 10 is still misclassified. Why is that?

In this tutorial, we’ll show you what it takes to be practical with Machine Learning. First, we’ll show you how to build a working environment for machine learning. Then you will immediately start working with your first machine learning model.

You have been classified countless times at school! The most popular application of text classification in machine learning is sentiment analysis, where texts are given an emotional label such as ‘positive’ or ‘negative’. However, there are many other text classification applications that can be realized today with machine learning. In the following, these five applications of text classification will be discussed: Continue reading “Applications of Text Classification in Machine Learning”

Machine Learning

The progress in machine learning is amazing today. And machines are getting more amazing every day. They clearly beat humans in games like chess and Go. They translate texts of ever better quality from one language to another. They can recognize and describe contents of audio, picture or video documents. Even in art, a domain that until recently was reserved for humans, they are impressive. Machine Learning has taken giant steps lately.

But how far do we understand this progress? And how do we deal with the ever-improving machine learning based computer programs in our everyday life? These questions arise both from the perspective of the user and the provider of such ‘intelligent’ services. Learning machine learning will become a central task of the individual and of society in the future.Continue reading “Learning Machine Learning”